docs: def find_bin(self, x):
          """
          Sort input or inputs according to the current bin estimate
  
          Parameters
          ----------
          x       :   array or numeric
                      a value or array of values to fit within the estimated
                      bins
  
          Returns
          -------
          a bin index or array of bin indices that classify the input into one of
          the classifiers' bins.
  
          Note that this differs from similar functionality in
          numpy.digitize(x, classi.bins, right=True).
  
          This will always provide the closest bin, so data "outside" the classifier,
          above and below the max/min breaks, will be classified into the nearest bin.
  
          numpy.digitize returns k+1 for data greater than the greatest bin, but retains 0
          for data below the lowest bin.
          """
          x = np.asarray(x).flatten()
          right = np.digitize(x, self.bins, right=True)
          if right.max() == len(self.bins):
              right[right == len(self.bins)] = len(self.bins) - 1
          return right